Search Results (3,130)

The increasing threat of salinity, exacerbated by climate change and unsustainable agricultural practices, necessitates innovative and sustainable crop management strategies to safeguard vegetable crop production and global food security. This review highlights a comprehensive framework that combines physiological insights with practical interventions aimed at enhancing salinity tolerance in vegetable crops. Key strategies include grafting, precision irrigation and fertilization, biofortification, and biostimulant application. These practices are applicable to both soil-based and soilless cultivation systems, offering broad relevance across diverse production environments. Combining and adapting these strategies to specific crops and environments is essential for developing sustainable, productive vegetable farming systems that can survive rising salinity and secure future food supplies. Future research focus on optimizing these integrated methods and elucidating their underlying mechanisms to enable wider and more effective adoption. Full article

Saline–alkaline water resources are globally widespread, and their rational development offers significant potential to alleviate freshwater scarcity. Saline–alkaline water aquaculture farming not only affects fish growth and survival but also impairs reproductive and developmental functions. Largemouth bass (Micropterus salmoides), an economically important fish, has demonstrated excellent high tolerance to such environments, in order to investigate the effects of alkaline water aquaculture environments on its growth performance, sex hormone levels, gonadal development, and molecular adaptation mechanisms. In this study, largemouth bass were chronically exposed to freshwater (0.55 mmol/L), low alkalinity (10 mmol/L), or high alkalinity (25 mmol/L) and cultured for 80 days. Alkalinity exposure more severely impacted the growth rate of females. High alkalinity significantly increased the hepatosomatic index and decreased the gonadosomatic index in both sexes; moreover, it induced oxidative stress in both sexes, evidenced by reduced superoxide dismutase (SOD), catalase (CAT), and total antioxidant capacity (TAOC) levels and elevated malondialdehyde (MDA) content. Furthermore, the levels of sex hormones Serum estradiol (E2), 11-ketotestosterone (11-KT), and testosterone were significantly reduced, accompanied by either an elevated ratio of primary oocytes and follicular atresia, or by reduced spermatogenesis. Apoptotic signals appeared in gonadal interstitial cells, with upregulated expression of genes P53, Bax, Casp3, and Casp8. Ultrastructural damage included fewer mitochondria and cristae blurring, further indicating tissue damage causing dysfunction. Transcriptome results showed that oxidative stress damage and energy metabolism imbalance caused by carbonate alkalinity were key to the delayed gonadal development, which was mainly manifested in enrichment of the ECM–receptor interaction and PI3K-Akt signaling pathways in females exposed to low alkalinity, and the GnRH secretion and chemokine signaling pathways in males. Glycosphingolipid biosynthesis and Ferroptosis pathway were enriched in females exposed to high alkalinity, and the Cortisol synthesis and secretion pathway were enriched in males. Overall, high-alkalinity exposure significantly delayed gonadal development in both sexes of largemouth bass, leading to reproductive impairment. Full article

As global food demand continues to rise, conventional agricultural practices face increasing difficulty in sustainably meeting production requirements. In response, deep learning-driven automated systems have emerged as promising solutions for enhancing precision farming. Nevertheless, accurate fruit segmentation remains a significant challenge in orchard environments due to factors such as occlusion, background clutter, and varying lighting conditions. This study proposes the Depthwise Asymmetric Bottleneck with Attention Mechanism Network (DABAMNet), an advanced convolutional neural network (CNN) architecture composed of multiple Depthwise Asymmetric Bottleneck Units (DABou), specifically designed to improve apple segmentation in RGB imagery. The model incorporates the Convolutional Block Attention Module (CBAM), a dual attention mechanism that enhances channel and spatial feature discrimination by adaptively emphasizing salient information while suppressing irrelevant content. Furthermore, the CBAM attention module employs multiple global pooling strategies to enrich feature representation across varying spatial resolutions. Through comprehensive ablation studies, the optimal configuration was identified as early CBAM placement after DABou unit 5, using a reduction ratio of 2 and combined global max-min pooling, which significantly improved segmentation accuracy. DABAMNet achieved an accuracy of 0.9813 and an Intersection over Union (IoU) of 0.7291, outperforming four state-of-the-art CNN benchmarks. These results demonstrate the model’s robustness in complex agricultural scenes and its potential for real-time deployment in fruit detection and harvesting systems. Overall, these findings underscore the value of attention-based architectures for agricultural image segmentation and pave the way for broader applications in sustainable crop monitoring systems. Full article

Digital twin technology is reshaping modern agriculture. Digital twins are the virtual replicas of real-world farming systems, which are continuously updated with real-time data, and are revolutionizing the monitoring, simulation, and optimization of agricultural processes. The literature on agricultural digital twins is multidisciplinary, growing rapidly, and often fragmented across disciplines, which lacks well-curated documentation. A bibliometric analysis includes thematic content analysis and science mapping, which provides research trends, gaps, thematic landscape, and key contributors in this continuously evolving and emerging field. Therefore, in this study, we conducted a bibliometric review that included collecting bibliometric data via keyword search strategies on popular scientific databases. The data was further screened, processed, analyzed, and visualized using bibliometric tools to map research trends, landscapes, collaborations, and themes. Key findings show that publications have grown exponentially since 2018, with an annual growth rate of 27.2%. The major contributing countries were China, the USA, the Netherlands, Germany, and India. We observed a collaboration network with distinct geographic clusters, with strong intra-European ties and more localized efforts in China and the USA. The analysis identified seven major research theme clusters revolving around precision farming, Internet of Things integration, artificial intelligence, cyber–physical systems, controlled-environment agriculture, sustainability, and food system applications. We observed that core technologies, such as sensors, artificial intelligence, and data analytics, have been extensively explored, while identifying gaps in research areas. The emerging interests include climate resilience, renewable-energy integration, and supply-chain optimization. The observed transition from task-specific tools to integrated, system-level approaches underline the growing need for adaptive, data-driven decision support. By outlining research trends and identifying strategic research gaps, this review offers insights into leveraging digital twins to improve productivity, sustainability, and resilience in global agriculture. Full article

Digital livestock farming faces a critical deployment challenge: bridging the gap between cutting-edge AI algorithms and practical implementation in resource-constrained agricultural environments. While deep learning models demonstrate exceptional accuracy in laboratory settings, their translation to operational farm systems remains limited by computational constraints, connectivity issues, and user accessibility barriers. Dairy DigiD addresses these challenges through a novel edge-cloud AI framework integrating YOLOv11 object detection with DenseNet121 physiological classification for cattle monitoring. The system employs YOLOv11-nano architecture optimized through INT8 quantization (achieving 73% model compression with <1% accuracy degradation) and TensorRT acceleration, enabling 24 FPS real-time inference on NVIDIA Jetson edge devices while maintaining 94.2% classification accuracy. Our key innovation lies in intelligent confidence-based offloading: routine detections execute locally at the edge, while ambiguous cases trigger cloud processing for enhanced accuracy. An entropy-based active learning pipeline using Roboflow reduces the annotation overhead by 65% while preserving 97% of the model performance. The Gradio interface democratizes system access, reducing technician training requirements by 84%. Comprehensive validation across ten commercial dairy farms in Atlantic Canada demonstrates robust performance under diverse environmental conditions (seasonal, lighting, weather variations). The framework achieves mAP@50 of 0.947 with balanced precision-recall across four physiological classes, while consuming 18% less energy than baseline implementations through attention-based optimization. Rather than proposing novel algorithms, this work contributes a systems-level integration methodology that transforms research-grade AI into deployable agricultural solutions. Our open-source framework provides a replicable blueprint for precision livestock farming adoption, addressing practical barriers that have historically limited AI deployment in agricultural settings. Full article

Sustainability within the rice and wheat supply chain is integral to attaining the UN’s Sustainable Development Goals (SDGs), as they are the two most consumed grains as food. Rice and wheat cultivation significantly impacts the environment, with the agricultural sector employing 27% of the global workforce and contributing 4% to the world’s GDP, thereby affecting social and economic sustainability. Developing a sustainability index for the wheat and rice supply chain is a complex endeavor, as it depends on various factors such as the location of growers, farming methods, the target audience, and the stakeholders involved. This index must be derived from an optimal selection of indicators to avoid information overload while covering all essential sustainability aspects. There are different methods, such as life cycle assessment, energy analysis, ecological footprint, and carbon footprint, being used to assess sustainability, with indicator-based assessment emerging as a comprehensive approach. This study utilised the Triple Bottom Line (TBL) to identify optimal sustainability indicators in the wheat and rice supply chain. A systematic literature review was initially conducted, followed by an expert opinion survey to determine the required indicators. The literature review unveiled a wide array of indicators used across studies, often contingent on each study’s specific objectives. While some consistency existed in environmental indicators, discussions on social and economic dimensions within the wheat and rice supply chain were limited. Analysis of the expert opinion survey revealed a consensus on most selected indicators, albeit with variations based on experts’ geographical locations. The final set of optimal indicators identified can serve as a foundation for developing a sustainability index, implementing a sustainability information management system, and formulating policy initiatives in the rice and wheat supply chain. Full article

Water quality monitoring and evaluation are essential across multiple sectors, including public health, environmental protection, agriculture and livestock management, industrial processes, and broader sustainability efforts. Conventional water analysis techniques, although accurate, are often constrained by their labor-intensive nature, extended processing times, and limited applicability for in situ, real-time monitoring. In recent years, spectroscopy-based methods have gained prominence as alternatives for water quality assessment, particularly when combined with chemometric analyses and advanced technological systems. This review provides an overview of the current advancements of spectroscopy-based water monitoring, with a focus on spectroscopy techniques operating within ultraviolet–visible (UV–Vis) and infrared (IR) spectral regions, which are currently applied for the assessment of a broad range of physicochemical and biological parameters relevant to livestock water management, including chemical oxygen demand (COD), dissolved organic carbon (DOC), nitrates, microbial contamination, and heavy metal ions. The findings highlight the growing utility of spectroscopy as a reliable tool in water quality assessment (e.g., COD detection with R² = 0.86 and nitrate detection with R² = 0.95 compared to traditional methods) and underpin the need for continued research into scalable, sensor-integrated solutions tailored for use in livestock farming environments. Full article

Poultry is a vital component of global meat production, with particular importance in Nigeria and Africa, as it promotes food security, economic growth, and rural livelihoods. Indigenous chickens, although less productive, are well adapted to local environments and provide significant socio-economic and nutritional benefits. The rising demand for animal protein and concerns over antimicrobial resistance (AMR) necessitate the development of sustainable alternatives to antibiotics in poultry production. Probiotics have emerged as effective feed additives that enhance gut health, immunity, nutrient absorption, and overall productivity. While extensively studied in commercial poultry, research on probiotics in Nigerian Indigenous Ecotype Chickens (NIECs) remains limited. Key challenges in indigenous poultry systems include low productivity, disease vulnerability, limited veterinary access, and environmental pressures. Addressing these requires improved management practices, infrastructure, veterinary support, and enabling policies. Multi-strain probiotics, particularly those containing Lactobacillus, Bifidobacterium, and Bacillus species, demonstrate promise in enhancing productivity, improving product quality, promoting environmental sustainability, and ensuring food safety. Focused research on local probiotic strains, field trials, farmer education, and policy support is crucial for harnessing the full benefits of probiotics and transforming indigenous poultry farming into a resilient and sustainable sector. Full article

In crab pond environments, obstacles such as long aerobic pipelines, aerators, and ground cages are usually sparsely distributed. Automatic feeding boats can navigate while avoiding obstacles and execute feeding tasks along planned paths, thus improving feeding quality and operational efficiency. In large-scale crab pond farming, a single feeding operation often fails to achieve the complete coverage of the bait casting task due to the limited boat load. Therefore, this study proposes a multi-voyage path planning scheme for feeding boats. Firstly, a complete coverage path planning algorithm is proposed based on an improved genetic algorithm to achieve the complete coverage of the bait casting task. Secondly, to address the issue of an insufficient bait loading capacity in complete coverage operations, which requires the feeding boat to return to the loading wharf several times to replenish bait, a multi-voyage path planning algorithm is proposed. The return point of the feeding operation is predicted by the algorithm. Subsequently, the improved Q-Learning algorithm (I-QLA) is proposed to plan the optimal multi-voyage return paths by increasing the exploration of the diagonal direction, refining the reward mechanism and dynamically adjusting the exploration rate. The simulation results show that compared with the traditional genetic algorithm, the repetition rate, path length, and the number of 90° turns of the complete coverage path planned by the improved genetic algorithm are reduced by 59.62%, 1.27%, and 28%, respectively. Compared with the traditional Q-Learning algorithm, average path length, average number of turns, average training time, and average number of iterations planned by the I-QLA are reduced by 20.84%, 74.19%, 48.27%, and 45.08%, respectively. The crab pond experimental results show that compared with the Q-Learning algorithm, the path length, turning times, and energy consumption of the I-QLA algorithm are reduced by 29.7%, 77.8%, and 39.6%, respectively. This multi-voyage method enables efficient, low-energy, and precise feeding for crab farming. Full article

Campylobacter is the most common cause of bacterial foodborne illness in the EU, primarily linked to poultry. To better understand its transmission dynamics, we applied whole-genome sequencing (WGS) to Campylobacter isolates collected at slaughterhouses over a two-year period from broilers originating from 26 Danish farms. The samples included cloacal swabs and boot sock samples from broiler houses and surrounding farm environments. We identified 150 distinct cgMLST types among 883 isolates. While most cgMLST types were flock-specific, some persisted across production cycles or appeared at different farms, indicating entrenched contamination or potential common-source introductions. Notably, 39% of broiler-associated cgMLST types overlapped with human clinical isolates from the same period, with the strongest overlap among persistent and cross-farm types, particularly in conventional production systems. Our findings underscore the need for strengthened biosecurity, targeted surveillance of high-risk genotypes, and real-time WGS integration to mitigate the burden of human Campylobacteriosis. This study supports a One Health approach to managing zoonotic risk in poultry production. Full article

The intensive farming of aquaculture species such as red tilapia (Oreochromis spp.) across diverse production systems can lead to changes in genetic parameters and responses of economically important traits in this species. This study represents the first attempt to understand these changes in growth traits (body weight, total length), quality attributes (body colour), and survival rate in red tilapia. Data for these traits were collected from 75,950 individual fish, progeny of 970 full-sib families (comprising 970 dams and 486 sires); they were selected for high body weight and evaluated in two distinct culture environments: fresh- and saltwater ponds. A multi-trait mixed model was employed to estimate genetic parameters and selection responses. Genetic variance estimates for the quality and survival traits varied across the two environments. However, genetic correlations among the traits studied were similar between fresh and saline water. Furthermore, significant G × E interactions, particularly for the quality and survival traits, were evidenced by divergent genetic correlations (r_g = 0.57–0.83) between homologous traits across different environments. The findings emphasise the importance of incorporating G × E interactions into the selection program for red tilapia, particularly when the breeding objectives extend to include quality and survival traits. Selection strategies should consider the prevailing culture system—for instance, favouring genotypes suited to the freshwater pond environment over those adapted to the saltwater environment. Continual assessment of full-sib groups across these environments is recommended to refine our understanding of G × E interactions and optimise future breeding programs for red tilapia. This may involve selecting genotypes capable of consistent performance across environments or developing environment-specific breeding programs. Full article

Existing rice harvesting models often lack depth or extensibility and are limited in their scope across the agriculture value chain, from crop planting to postharvest handling. A multi-agent system (MAS) offers flexibility and scalability and supports the simulation and modeling of complex real-world scenarios. This paper introduces a novel approach utilizing an MAS to simulate rice harvesting operations (including additional pre- and post-harvesting operations). Initially, a generic MAS was created, and it was then subsequently adapted to the agricultural context of rice farming in Central Japan. The localized MAS consists of agents such as weather, farm, rice centers, fields, crops and multiple agriculture machinery. Additionally, the introduced MAS environment is based on a discrete event simulation that enables communication across various independent agents. The system includes different harvesting schedule policies which determine the harvesting order for multiple paddy fields on specific days. The system was evaluated through two distinct experiments: (i) ‘Model Verification Simulation’, which successfully demonstrated the replication of actual historical farming practices, and (ii) ‘Operational Efficiency Simulation’, which compared the overall farm efficiency under different scheduling policies as well as different environmental conditions (e.g., rainfall). The simulation successfully generated a dataset containing traits and performance indicators that replicate the patterns observed in real-world data, while also approximating the operational behaviors and workflows of actual rice harvesting systems. Future studies could further evaluate the model’s robustness to confirm its practical applicability. Full article

Mycotoxin contamination in animal feed can cause acute or chronic adverse effects on growth, productivity, and immune function in livestock. This study aimed to evaluate the impact of difructose anhydride III (DFA III) supplementation on serum biochemical parameters and intestinal environment in Japanese Black (JB) breeding cows under low-level chronic dietary exposure to zearalenone (ZEN). Using urinary ZEN concentration as an indicator of exposure, 25 JB cows were selected from a breeding farm with confirmed natural feed contamination. Blood samples were collected before DFA III supplementation (day 0), and on days 20 and 40 post-supplementation. Serum biochemical parameters and short-chain fatty acid concentrations were measured. During the studies, dietary ZEN concentration increased, yet improvements were observed in liver function, nutritional status, immune response, and inflammatory markers. Notably, serum butyrate concentration significantly increased following DFA III administration. These findings suggest that DFA III may positively influence intestinal microflora and enhance intestinal barrier function, which could contribute to improved health and nutritional status in cattle exposed to low-level chronic dietary ZEN contamination. DFA III supplementation may represent a promising strategy for mitigating the effects of low-level mycotoxin exposure in livestock production systems. Full article

Understanding the combined gravi-phototropic behavior of plants is essential for space agriculture. Existing single-axis clinostats and gel-based grow media provide limited simulation fidelity. This study developed a Cloud-enabled triple-axis clinostat with built-in automated aeroponic and artificial photosynthetic lighting systems for Earth-based simulation under Martian gravity ranging from 0.35 to 0.4 g. Finite element analysis validated the stability and reliability of the acrylic and stainless steel rotating platform based on stress, strain, and thermal simulation tests. Arduino UNO microcontrollers were used to acquire and process sensor data to activate clinorotation and controlled environment systems. An Arduino ESP32 transmits grow chamber temperature, humidity, moisture, light intensity, and gravity sensor data to ThingSpeak and the Create IoT online platform for seamless monitoring and storage of enviro-physical data. The developed system can generate 0.252–0.460 g that suits the target Martian gravity. The combined gravi-phototropic tests confirmed that maize seedlings exposed to partial gravity and grown using the aeroponic approach have a shoot system growth driven by light availability (395–400 μmol/m²/s) across the partial gravity extremes. Root elongation is more responsive to gravity increase under higher partial gravity (0.375–0.4 g) even with low light availability. The developed soilless clinostat technology offers a scalable tool for simulating other high-value crops aside from maize. Full article

Drones have been widely used in precision agriculture to capture high-resolution images of crops, providing farmers with advanced insights into crop health, growth patterns, nutrient deficiencies, and pest infestations. Although several machine and deep learning models have been proposed for plant stress and disease detection, their performance regarding accuracy and computational time still requires improvement, particularly under limited data. Therefore, this paper aims to address these challenges by conducting a comparative analysis of three State-of-the-Art object detection deep learning models: YOLOv8, RetinaNet, and Faster R-CNN, and their variants to identify the model with the best performance. To evaluate the models, the research uses a real-world dataset from potato farms containing images of healthy and stressed plants, with stress resulting from biotic and abiotic factors. The models are evaluated under limited conditions with original data of size 360 images and expanded conditions with augmented data of size 1560 images. The results show that YOLOv8 variants outperform the other models by achieving larger mAP@50 values and lower inference times on both the original and augmented datasets. The YOLOv8 variants achieve mAP@50 ranging from 0.798 to 0.861 and inference times ranging from 11.8 ms to 134.3 ms, while RetinaNet variants achieve mAP@50 ranging from 0.587 to 0.628 and inference times ranging from 118.7 ms to 158.8 ms, and Faster R-CNN variants achieve mAP@50 ranging from 0.587 to 0.628 and inference times ranging from 265 ms to 288 ms. These findings highlight YOLOv8’s robustness, speed, and suitability for real-time aerial crop monitoring, particularly in data-constrained environments. Full article